An improved energy optimization model for sensitive IoT devices using clustered machine learning

The rise of the Internet of Things (IoT) has been emphasized in range-sensitive devices like medical sensors and environmental monitors, which need durable battery life alongside reliable performance. However, the limited resources of these devices make energy optimization especially difficult. Current energy optimization models for IoT devices often use simplistic techniques that might only partially capture these devices' diverse range of energy profiles. In this paper, an enhanced machine learning-based optimization model is designed for clustered energy conservation to manage the AEC vulnerabilities of IoT devices. The model employs a clustering method to group the devices with similar energy usage patterns, which helps better predict and optimize total load across clusters. The model also uses machine learning algorithms to evolve with usage patterns and change energy optimization strategies accordingly. This strategy has been applied to a wide range of low-power IoT devices. It has shown substantial savings in energy utilization, making them much more efficient than conventional optimization methods. In conclusion, we argue how our design enables scalable and customizable solutions for energy-efficient maintenance of IoT devices running sensitive computations. We use machine learning and clustering techniques that are custom-built for the energy management of these devices, ensuring long battery life and reliable performance even under heavy workloads or critical applications. This may also accelerate the adoption and evolution of those security IoT devices across different areas, increasing their effects on society.